Neuroimage analysis requires the processing of image data through “pipelines”, composed of multiple different steps (Zijdenbos, A. P., R. Forghani, and A. C. Evans. IEEE Trans Med Imaging, 2002. 21(10): p. 1280-91.). In large-scale longitudinal studies, pipelines generate thousands of intermediary and final images. Each step must be verified to ensure proper completion, a process referred to as quality control. Further, each process performed needs to be evaluated for correctness, a process referred to as quality assurance. Typically, quality control and assurance is performed visually (Simmons, A., et al., Int J Geriatr Psychiatry. 26(1): p. 75-82.), but this is increasingly prohibitive.
This problem became particularly acute as applicants analyzed MRIs from the large, multi-centric Alzheimer's disease Neuroimaging Initiative (ADNI). In ADNI, MRIs were acquired for >800 subjects at every 6 months over 3 years. Pipelining the whole data set for even simple analyses, e.g. voxel-based morphometry, resulted in more than 40,000 images for review. Applicants' objective was to devise an automated quality control and assurance system able to assess every pipeline step.
Automated image-processing pipelines must be robust to variations, if they are to provide reliable and reproducible measurements that have clinical meaning. Once images have been properly processed, they are better starting material for further processes in a pipeline where the ultimate objective can be diagnosing diseases such as multiple sclerosis (Zijdenbos et al.) or Alzheimer's disease (Duchesne et al., PCT/CA2010/000140).
Zijdenbos et al. suggest that, while fully automated quality control is desirable, visual inspection is still necessary in order to perform quality assurance, i.e. ensure that the processing step achieved the required performance level.
The drawbacks of current methods for analysing large volumes of image data causes human intervention to be time consuming and inefficient. There is a need for methods and apparatuses that have better quality control/assurance, leading to the absence of human intervention, or at least leading to less human intervention for final quality decisions.